A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.

Stable carbon isotopic analyses of n-alkanes and the calculated C4 plant-derived fractions in the dust samples

Atmospheric dust samples collected along a transect off the West African coast have been investigated for their lipid content and compound-specific stable carbon isotope compositions. The saturated hydrocarbon fractions of the organic solvent extracts consist mainly of long-chain n-alkanes derived from epicuticular wax coatings of terrestrial plants. Backward trajectories for each sampling day and location were calculated using a global atmospheric circulation model. The main atmospheric transport took place in the low-level trade-wind layer, except in the southern region, where long-range transport in the mid-troposphere occurred. Changes in the chain length distributions of the n-alkane homologous series are probably related to aridity, rather than temperature or vegetation type. The carbon preference of the leaf-wax n-alkanes shows significant variation, attributed to a variable contribution of fossil fuel- or marine-derived lipids. The effect of this nonwax contribution on the d13C values of the two dominant n-alkanes in the aerosols, n-C29 and n-C31 alkane, is, however, insignificant. Their d13C values were translated into a percentage of C4 vs. C3 plant type contribution, using a two-component mixing equation with isotopic end-member values from the literature. The data indicate that only regions with a predominant C4 type vegetation, i.e. the Sahara, the Sahel, and Gabon, supply C4 plant-derived lipids to dust organic matter. The stable carbon isotopic compositions of leaf-wax lipids in aerosols mainly reflect the modern vegetation type along their transport pathway. Wind abrasion of wax particles from leaf surfaces, enhanced by a sandblasting effect, is most probably the dominant process of terrigenous lipid contribution to aerosols.

Overview on bio-based building material made with plant aggregate

Global warming, energy savings, and life cycle analysis issues are factors that have contributed to the rapid expansion of plant-based materials for buildings, which can be qualified as environmental-friendly, sustainable and efficient multifunctional materials. This review presents an overview on the several possibilities developed worldwide about the use of plant aggregate to design bio-based building materials. The use of crushed vegetal aggregates such as hemp (shiv), flax, coconut shells and other plants associated to mineral binder represents the most popular solution adopted in the beginning of this revolution in building materials. Vegetal aggregates are generally highly porous with a low apparent density and a complex architecture marked by a multi-scale porosity. These geometrical characteristics result in a high capacity to absorb sounds and have hygro-thermal transfer ability. This is one of the essential characteristics which differ of vegetal concrete compared to the tradition mineral-based concretes. In addition, the high flexibility of the aggregates leads to a non-fragile elasto-plastic behavior and a high deformability under stress, lack of fracturing and marked ductility with absorbance of the strains ever after having reached the maximum mechanical strength. Due to the sensitivity to moisture, the assessment of the durability of vegetal concrete constitutes one of the next scientific challenging of bio-based building materials.

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Session organiser/chair: The architecture of coal and other energies

Coal ignited the industrial revolution. An organic sedimentary rock that energized the globe, transforming cities, landscapes and societies for generations, the importance of ‘King Coal’ to the development and consolidation of modernity has been well-recognised. And yet, as a critical factor in the production of modern architecture, coal—as well as other forms of energy—has been mostly overlooked.

From Appalachia to Lanarkshire, from the pits of northern France, Belgium and the Ruhr valley, to the monumental opencast excavations of Russia, China, Africa and Australia, mining operations have altered the immediate social and physical landscapes of coal-rich areas. But in contrast to its own underground conditions of production, the winning of coal, especially in the twentieth-century, has produced conspicuously enlightened and humane approaches to architecture and urbanism. In the twentieth century, educational buildings, holiday camps, hospitals, swimming pools, convalescent homes and housing prevailed alongside model collieries in mining settlements and areas connected to them. In 1930s Britain, pit head baths—funded by a levy on each ton produced—were often built in the International Style. Many won praise for architectural merit, appearing in Nicholas Pevsner’s guides to the buildings of England alongside cathedrals, village manors and Masonic halls as testimonies to the public good.

The deep relationships between coal and modernity, and the expressions of architecture it has articulated, in the collieries from which it was hewn, the landscape and towns it shaped, and the power stations and other infrastructure where it was used, offer innumerable opportunities to explore how coal produced architectures which embodied and expressed both social and technological conditions. While proposals on coal are preferred, we also welcome papers that interrogate the complexity, heterogeneity and hybridity of other forms of energy production and how these have also interceded into architectural form at a range of scales.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Model-driven development of sensor network applications with optimization of non-functional constraints

Wireless sensor networks (WSNs) differ from conventional distributed systems in many aspects. The resource limitation of sensor nodes, the ad-hoc communication and topology of the network, coupled with an unpredictable deployment environment are difficult non-functional constraints that must be carefully taken into account when developing software systems for a WSN. Thus, more research needs to be done on designing, implementing and maintaining software for WSNs. This thesis aims to contribute to research being done in this area by presenting an approach to WSN application development that will improve the reusability, flexibility, and maintainability of the software. Firstly, we present a programming model and software architecture aimed at describing WSN applications, independently of the underlying operating system and hardware. The proposed architecture is described and realized using the Model-Driven Architecture (MDA) standard in order to achieve satisfactory levels of encapsulation and abstraction when programming sensor nodes. Besides, we study different non-functional constrains of WSN application and propose two approaches to optimize the application to satisfy these constrains. A real prototype framework was built to demonstrate the developed solutions in the thesis. The framework implemented the programming model and the multi-layered software architecture as components. A graphical interface, code generation components and supporting tools were also included to help developers design, implement, optimize, and test the WSN software. Finally, we evaluate and critically assess the proposed concepts. Two case studies are provided to support the evaluation. The first case study, a framework evaluation, is designed to assess the ease at which novice and intermediate users can develop correct and power efficient WSN applications, the portability level achieved by developing applications at a high-level of abstraction, and the estimated overhead due to usage of the framework in terms of the footprint and executable code size of the application. In the second case study, we discuss the design, implementation and optimization of a real-world application named TempSense, where a sensor network is used to monitor the temperature within an area.

Study of Worker’s Exposure to Tantalum-bearing Particles in a Mining and Metallurgical Plant

The objective of this study was to assess worker exposure to mineral dust particles and a metabolic model, based on the model adopted by ICRP, was applied to assess human exposure to Ta, and predicted values of Ta concentrations in excreta. The occupational exposure to Th, U, Nb, and Ta bearing particles during routine tasks to obtain Fe-Nb alloys was estimated using air samplers and excreta samples. Ta concentrations in food samples and in drinking water were also determined. The results support that workers were occupationally exposed to Ta bearing particles, and also indicate that a source of Ta exposure for both workers and the control group was the ingestion of drinking water containing soluble compounds of Ta. Therefore, some Ta compounds should be considered soluble compounds in gastrointestinal tract. Consequently the metabolic model based on ICRP metabolic model and/or the transfer factor f1 for Ta should be reviewed and the solubility of Ta compounds in gastrointestinal should be determined.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.

Federated architecture for biomedical data integration

The last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.

Development of a model to predict the life of pneumatic conveyor bends subject to erosive wear

The erosion processes resulting from flow of fluids (gas-solid or liquid-solid) are encountered in nature and many industrial processes. The common feature of these erosion processes is the interaction of the fluid (particle) with its boundary thus resulting in the loss of material from the surface. This type of erosion in detrimental to the equipment used in pneumatic conveying systems. The puncture of pneumatic conveyor bends in industry causes several problems. Some of which are: (1) Escape of the conveyed product causing health and dust hazard; (2) Repairing and cleaning up after punctures necessitates shutting down conveyors, which will affect the operation of the plant, thus reducing profitability. The most common occurrence of process failure in pneumatic conveying systems is when pipe sections at the bends wear away and puncture. The reason for this is particles of varying speed, shape, size and material properties strike the bend wall with greater intensity than in straight sections of the pipe. Currently available models for predicting the lifetime of bends are inaccurate (over predict by 80%. The provision of an accurate predictive method would lead to improvements in the structure of the planned maintenance programmes of processes, thus reducing unplanned shutdowns and ultimately the downtime costs associated with these unplanned shutdowns. This is the main motivation behind the current research. The paper reports on two aspects of the first phases of the study-undertaken for the current project. These are (1) Development and implementation; and (2) Testing of the modelling environment. The model framework encompasses Computational Fluid Dynamics (CFD) related engineering tools, based on Eulerian (gas) and Lagrangian (particle) approaches to represent the two distinct conveyed phases, to predict the lifetime of conveyor bends. The method attempts to account for the effect of erosion on the pipe wall via particle impacts, taking into account the angle of attack, impact velocity, shape/size and material properties of the wall and conveyed material, within a CFD framework. Only a handful of researchers use CFD as the basis of predicting the particle motion, see for example [1-4] . It is hoped that this would lead to more realistic predictions of the wear profile. Results, for two, three-dimensional test cases using the commercially available CFD PHOENICS are presented. These are reported in relation to the impact intensity and sensitivity to the inlet particle distributions.

Plant Size as Determinant of Species Richness of Herbivores, Natural Enemies and Pollinators across 21 Brassicaceae Species

Large plants are often more conspicuous and more attractive for associated animals than small plants, e.g. due to their wider range of resources. Therefore, plant size can positively affect species richness of associated animals, as shown for single groups of herbivores, but studies usually consider intraspecific size differences of plants in unstandardised environments. As comprehensive tests of interspecific plant size differences under standardised conditions are missing so far, we investigated effects of plant size on species richness of all associated arthropods using a common garden experiment with 21 Brassicaceae species covering a broad interspecific plant size gradient from 10 to 130 cm height. We recorded plant associated ecto-and endophagous herbivores, their natural enemies and pollinators on and in each aboveground plant organ, i.e. flowers, fruits, leaves and stems. Plant size (measured as height from the ground), the number of different plant organ entities and their biomass were assessed. Increasing plant size led to increased species richness of associated herbivores, natural enemies and pollinating insects. This pattern was found for ectophagous and endophagous herbivores, their natural enemies, as well as for herbivores associated with leaves and fruits and their natural enemies, independently of the additional positive effects of resource availability (i.e. organ biomass or number of entities and, regarding natural enemies, herbivore species richness). We found a lower R-2 for pollinators compared to herbivores and natural enemies, probably caused by the high importance of flower characteristics for pollinator species richness besides plant size. Overall, the increase in plant height from 10 to 130 cm led to a 2.7-fold increase in predicted total arthropod species richness. In conclusion, plant size is a comprehensive driver of species richness of the plant associated arthropods, including pollinators, herbivores and their natural enemies, whether they are endophagous or ectophagous or associated with leaves or fruits.

Architecture for IoT Node and Platform

Only recently, during the past five years, consumer electronics has been evolving rapidly. Many products have started to include “smart home” capabilities, enabling communication and interoperability of various smart devices. Even more devices and sensors can be remote controlled and monitored through cloud services. While the smart home systems have become very affordable to average consumer compared to the early solutions decades ago, there are still many issues and things that need to be fixed or improved upon: energy efficiency, connectivity with other devices and applications, security and privacy concerns, reliability, and response time. This paper focuses on designing Internet of Things (IoT) node and platform architectures that take these issues into account, notes other currently used solutions, and selects technologies in order to provide better solution. The node architecture aims for energy efficiency and modularity, while the platform architecture goals are in scalability, portability, maintainability, performance, and modularity. Moreover, the platform architecture attempts to improve user experience by providing higher reliability and lower response time compared to the alternative platforms. The architectures were developed iteratively using a development process involving research, planning, design, implementation, testing, and analysis. Additionally, they were documented using Kruchten’s 4+1 view model, which is used to describe the use cases and different views of the architectures. The node architecture consisted of energy efficient hardware, FC3180 microprocessor and CC2520 RF transceiver, modular operating system, Contiki, and a communication protocol, AllJoyn, used for providing better interoperability with other IoT devices and applications. The platform architecture provided reliable low response time control, monitoring, and initial setup capabilities by utilizing web technologies on various devices such as smart phones, tablets, and computers. Furthermore, an optional cloud service was provided in order to control devices and monitor sensors remotely by utilizing scalable high performance technologies in the backend enabling low response time and high reliability.

Combining Leaf Shape and Texture for Costa Rican Plant Species Identification

In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap’s underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of , the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research